A point cloud registration network with dual-branch multi-level feature fusion， DMFNet （Dual-branch Multi-level Feature Fusion Network）， was proposed to address poor registration accuracy and insufficient robustness problems in point cloud registration caused by partial overlapping， occlusion， and noise interference. In the network， during the encoding stage， a rotation branch and a translation branch were configured in parallel， and self-attention fusion as well as cross-attention fusion modules were inserted at shallow， middle， and deep layers， so as to achieve multi-scale feature interaction and deep fusion between source and reference point clouds. At the same time， a rotation-translation feature fusion module was designed to strengthen pose estimation capability. During the regression stage， based on the lightweight Set Transformer regressor， multi-layer induced-attention blocks and an attention-pooling module were used to regress the quaternion and the translation vector directly. DMFNet does not rely on overlap-region detection or explicit mask estimation， and it shows strong adaptability and generalization. Comparative experiments with six point cloud registration methods were conducted on the ModelNet40 dataset， and a generalization study was carried out on the Stanford 3D scanning dataset. The results show that on the ModelNet40 dataset under noisy conditions and two-time sampling， DMFNet reduces RMSE（t） and Error（R） by 21.32% and 14.47% compared with MAximum Clique-based registration （MAC） method， respectively， and demonstrates superior robustness and registration accuracy.